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Title:
Ustvarjanje modela rezalnih sil z uporabo umetne inteligence
Authors:
ID
Milfelner, Matjaž
(
Author
)
ID
Župerl, Uroš
(
Author
)
ID
Čuš, Franc
(
Author
)
Files:
http://www.dlib.si/details/URN:NBN:SI:doc-NZJD8KIF
Language:
Slovenian
Work type:
Not categorized
Typology:
1.01 - Original Scientific Article
Organization:
FS - Faculty of Mechanical Engineering
Abstract:
Napovedovanje rezalnih sil pri frezanju z oblikovnim krogelnim frezalom je zelo pomembno za določitev optimalnih rezalnih parametrov pri postopku frezanja. Razviti modeli rezalnih sil pri frezanju z oblikovnim krogelnim frezalom, ki so predstavljeni v raziskavah, temeljijo na analitičnih metodah in so določeni z uporabo teoretičnega in praktičnega znanja ter preizkusov. V prispevku je predstavljen razvoj genetskega modela rezalnih sil za oblikovno krogelno frezalo z umetno inteligenco (genetsko programiranje). V genetskem modelu so upoštevani vsi vplivni parametri, ki vplivajo na velikost rezalne sile med postopkom frezanja. Predstavljeni model je ustvarjen iz preizkusnih podatkov za jeklo Ck45 pri različnih rezalnih parametrih. Dobljeni rezultati prikazujejo, da genetski model rezalne sile ustreza preizkusnim podatkom.
Publisher:
Association of Mechanical Engineers and Technicians of Slovenia et al.
Year of publishing:
2005
Number of pages:
str. 41-54
Numbering:
Letn. 51, št. 1
PID:
20.500.12556/DKUM-52991
UDC:
621.914:004.8
ISSN on article:
0039-2480
COBISS.SI-ID:
8034075
NUK URN:
URN:SI:UM:DK:T1U5VAZD
Publication date in DKUM:
10.07.2015
Views:
1495
Downloads:
51
Metadata:
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Record is a part of a journal
Title:
Strojniški vestnik
Shortened title:
Stroj. vestn.
Publisher:
Zveza strojnih inženirjev in tehnikov Slovenije [etc.], = Association of Mechanical Engineers and Technicians of Slovenia [etc.]
ISSN:
0039-2480
COBISS.SI-ID:
762116
Secondary language
Language:
English
Title:
Generation of a model for cutting forces using artificial intelligence
Abstract:
Being able to predict the cutting forces during milling with a ball-end milling cutter is very important for determining the optimal cutting parameters in the milling process. The already developed models of cutting forces in ball-end milling are based on analytical methods and are determined by means of theoretical and practical knowledge as well as experiments. This paper presents the development of a genetic model of cutting forces for a ball-end milling cutter using artificial intelligence (genetic programming). In the genetic model, all the parameters influencing the size of the cutting forces during the milling process are considered. The presented model is generated from experimental data for Ck45 steel with different cutting parameters. The results indicate that the genetic model of the cutting force agrees with the experimental data.
Keywords:
genetic models
,
cutting forces
,
milling
,
ball-end mill
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